Mikrochim Acta. 2025 Jun 16;192(7):428. doi: 10.1007/s00604-025-07286-8.

ABSTRACT

Cancer remains one of the most deadly diseases in the world, requiring constant growth and improvements in therapeutic strategies. Traditional cancer treatments, such as chemotherapy, radiotherapy, and surgery, have limitations like off-target release, toxicity, and inefficient drug delivery. This study explains the role of bioinformatics and AI in optimizing and analyzing liposomal formulations for innovative and better cancer therapy. Molecular docking (MD), molecular dynamics simulations, and machine learning models are the computational techniques that can help to design stable liposomal carriers for drugs, predict receptor-ligand interactions, and can improve drug release efficiency. Improved liposome nanoparticles (LNPs) surface functionalization, the discovery of tumor-specific biomarkers, and the improvement of receptor-ligand interactions for accurate drug targeting are all made possible by bioinformatics tools and methodologies. Moreover, AI-assisted predictions and in silico modeling can speed up drug discovery and processing while eliminating the experimental expenditures and time. In the present review, we conducted MD studies to complement the discussed literature. MD was performed between cyclic RGD peptides (liposomal ligands) and the GPR116 receptor in triple-negative breast cancer, and between folic acid (liposomal ligand) and the Axl tyrosine kinase receptor for lung cancer, revealing strong and stable interactions and highlighting the amino acid residues involved. Notwithstanding current obstacles, computational tools have shown notable progress in nanomedicine, exploring more options for more individualized and effective cancer therapies. The combination of AI, machine learning, and multi-omics techniques to improve therapeutic efficacy and reduce side effects is a substantial key to the future of LNP-based cancer treatment.

PMID:40523994 | DOI:10.1007/s00604-025-07286-8

Infect Agent Cancer. 2025 Jun 15;20(1):37. doi: 10.1186/s13027-025-00672-0.

ABSTRACT

Mendelian Randomization (MR) is increasingly used in cancer research to infer causal relationships by leveraging genetic variants as instrumental variables. While the growth of genome-wide association studies and biobank data has expanded the utility of MR, this surge-particularly pronounced in China-raises concerns about methodological rigor. The widespread adoption may be partly driven by the Chinese translation of key MR literature. Recent advances such as multivariable MR, mediation analysis, and integration with AI and omics data have enhanced the robustness and biological interpretability of MR studies. However, challenges persist, including horizontal pleiotropy, weak instrument bias, and misinterpretation of biomarkers as causal exposures. To improve MR study credibility, frameworks like STROBE-MR and MR-GRADE are being adopted. This article reviews methodological improvements and persistent pitfalls in MR, especially within cancer epidemiology, and highlights strategies for ensuring validity in this rapidly evolving field.

PMID:40518507 | PMC:PMC12168377 | DOI:10.1186/s13027-025-00672-0

Comput Biol Med. 2025 Aug;194:110559. doi: 10.1016/j.compbiomed.2025.110559. Epub 2025 Jun 14.

ABSTRACT

BACKGROUND: The in silico analyses provide evidence supporting the potential of methylation-driven differentially expressed genes as therapeutic targets across cancer types. This leads us to identify novel targets and their associated drug compounds for further progress towards pancreatic cancer treatment.

OBJECTIVE: To identify targeted drugs based on methylation driven genes identified using bulk multi-omics data and single-cell level data to pinpoint important disease markers.

METHODS: The workflow involves screening using the TCGA and ICGC databases, followed by validation with GEO datasets. The study employs supervised learning algorithms like kNN and random forests, and constructs a prediction model using adaptive LASSO-Cox regression. The process also includes pathway analysis, evaluation of survival status, and immune profile deconvolution, as well as multistage evaluation of the methylation driven genes. We conducted drug targeting and molecular dynamic simulations, taking into account genes of interest.Lastly, molecular docking and dynamics simulations were used to find out if the key MEDEGs could be utilized as drug targets.

RESULTS: CD36, UGT1A1, TFF1, S100P, MUC13, CALHM3 and ANKRD44 were found to be top 7 methylation driven genes. The mutational profile was also documented along with pathway analysis, which showed concordance with our observation based on their significant enriched terms namely "Maintenance of Gastrointestinal Epithelium", and "Digestive System Homeostasis". CD36 had prognostic capabilities and was seen to significant in terms of survival and also showed significant immune dysregulation. Our novel findings suggest TFF1, S100P, and MUC13 were found to be associated with cell type specific expression as seen in single cell data and UGT1A1 was found to be suitable for probable drug targeting. CD36, UGT1A1, TFF1, S100P, and MUC13 showed concordance when observed at proteomics level and across other datasets. Apigenin-7-O-glucuronide emerged as the top binder for UDP-glucuronosyltransferase 1A1 (also known as UDP 1A1), forming stable complexes with favourable interactions. Catechin and epicatechin were identified as the best ligands for TFF1 and S100P, while rutin showed high-affinity binding to MUC13.

CONCLUSION: The study successfully identified and validated a panel of biomarkers specific to pancreatic cancer, with potential applications in early diagnosis and treatment. The findings highlight the importance of multi-omics data integration in cancer research and the potential of personalized medicine in improving patient outcomes. The in-silico drug targeting analysis provides a foundation for the development of novel drugs for PanCa treatment. Hence TFF1, S100P, MUC13, and UGT1A1 showcased themselves as most promising biomarkers and novel drug targets.

PMID:40517592 | DOI:10.1016/j.compbiomed.2025.110559